Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition | Zendy

Seyyed Abed Hosseini | Zendy; M-R. Akbarzadeh-T | Zendy; Mohammad Bagher Naghibi Sistani | Zendy

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Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition

Author(s) -

Seyyed Abed Hosseini,

M-R. Akbarzadeh-T,

Mohammad Bagher Naghibi Sistani

Publication year - 2013

Publication title -

international journal of intelligent systems and applications

Language(s) - English

Resource type - Journals

eISSN - 2074-9058

pISSN - 2074-904X

DOI - 10.5815/ijisa.2013.06.05

Subject(s) - electroencephalography , ictal , computer science , hurst exponent , pattern recognition (psychology) , epileptic seizure , artificial intelligence , classifier (uml) , adaptive neuro fuzzy inference system , epilepsy , neuroscience , mathematics , statistics , psychology , fuzzy control system , fuzzy logic

A chaos-ANFIS approach is presented for analysis of EEG signals for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the hurst exponent (H) and largest lyapunov exponent (λ). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H and λ measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. Our inter-ictal EEG based diagnostic approach achieves 97.4% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 96.9% accuracy. Therefore, our method can be successfully applied to both inter-ictal and ictal data

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